Data Science Institute

Most Recent
Most Downloaded

Hybrid variational autoencoder for time series forecasting

Cai, B Yang, S Gao, L Xiang, Y

Variational autoencoders (VAE) are powerful generative models that learn the latent representations of input data as random variables. Recent studies show that VAE can flexibly learn the complex te...

Continue Reading
Assessing the role of global food commodity prices in achieving the 2030 agenda for SDGs.

Shen, Y Chen, Y Shi, X An, Y Yang, M Qi, Y

Food plays a vital role in human sustenance and well-being, and the fluctuations in its price exert a significant impact on the attainment of the Sustainable Development Goals (SDGs) from social, e...

Continue Reading
A Bi-Level Optimization Model for Inter-Provincial Energy Consumption Transfer Tax in China

Zeng, L Zhang, W Yang, M

The serious energy crisis and environmental problems resulting from fossil energy excessive consumption have caused severe challenges to the control of energy consumption and intensity (dual contro...

Continue Reading
Incident duration prediction using a bi-level machine learning framework with outlier removal and intra-extra joint optimisation

Grigorev, A Mihaita, A-S Lee, S Chen, F

Continue Reading
Using machine learning and deep learning for traffic congestion prediction: A review

Mihaita, AS Li, Z Singh, H Sharma, N Tuo, M Ou, Y

Continue Reading
Bias-Tolerant Fair Classification

Zhang, Y Zhou, F Li, Z Wang, Y Chen, F

The label bias and selection bias are acknowledged as two reasons in data that will hinder the fairness of machine-learning outcomes. The label bias occurs when the labeling decision is disturbed b...

Continue Reading
Fair Representation Learning with Unreliable Labels

Zhang, Y Zhou, F Li, Z Wang, Y Chen, F

In learning with fairness, for every instance, its label can be systematically flipped to another class due to the practitioner's prejudice, namely, label bias. The existing well-studied fair repre...

Continue Reading
Domain Generalization by Learning and Removing Domain-specific Features

Ding, Y Wang, L Liang, B Liang, S Wang, Y Chen, F

Deep Neural Networks (DNNs) suffer from domain shift when the test dataset follows a distribution different from the training dataset. Domain generalization aims to tackle this issue by learning a ...

Continue Reading
Machine learning prediction to improve operational resilience in managing disinfection for Macarthur Water Supply System

Tian, H

Continue Reading
Domain Generalization by Learning and Removing Domain-specific Features

Ding, Y Wang, L Liang, B Liang, S Wang, Y Chen, F

Continue Reading

Data Science Institute

Browse by

Discover